Evaluating Impacts of Climate Change on Traditional Mexican Maize

• Climate change is expected heavily impact natural and human systems worldwide (Walther, G.-R. et al., 2002; IPCC 2007, 2013; Kang, Y, S. Khan 2009; Hoegh-Guldberg, O. 2010)
• The IPCC reports numerous negative impacts of climate change on domesticated crops regardless of the emission scenario implemented(Brown and Funk 2008; Ureta et al. 2012; IPCC 2013)
• Ensuring global food security in an increasingly warming world with exponentially increasing world population requires multidisciplinary research (Brown and Funk 2008; Ureta et al. 2012; IPCC 2013, 2015)

Five percent reduction in crop season, sensitivity to change, capacity to cope. Source: CGIAR.

• Maize (Zea mays subsp. mays L.) supplies a staple food crop for more than 200 million people worldwide (Nuss and Tanumihardjo 2010; Ureta et al. 2013)
• Climate change is expected to alter maize suitability, especially between the tropics (Ramirez-Cabral et al., 2017)
• Mexico, maize's C.O.D., harbors traditional maize landraces that account for 60% of maize genetic diversity globally (Ureta et al. 2013)
• Preservation of agrobiodiversity at centers of domestication critical for global food security (Thrupp 2000; Esquinas-Alcazar 2005; Ureta et al. 2013).
  

Source: The International Maize and Wheat Improvement Center (CIMMYT)

• > 9,000 years from Mexican annual teosinte (Z. mays ssp. Parviglumus and ssp. mexicana ) in highlands via artificial selection by indigenous cultures (Matsuoka et al. 2002; Kato et al. 2009)
• Maize landraces have been grown (Ruiz Corral et al. 2008):
  • from sea-level to 2,900 (m asl)
  • avg. growing temp 12.0 °C - 29.1 °C
  • avg. seasonal precip. 400 mm to 3555 mm
• Offer valuable genetic resources

Source: https://lacienciaysusdemonios.com

• As a domesticated crop, maize is intimately contingent upon societal influences:
  • Seed management practices (Anderson 1947; Dyer and Lopez-Feldman 2013)
  • Indigenous religious practices (R. Ortega-Paczka., 2003)
  • Ethnolinguistic diversity (Perales, Benz, and Brush 2005; Brush and Perales 2007; Rivero-Romero et al. 2016)
  • Indigenous agricultural knowledge (Garcia-Martinez et al. 2016; Rivero-Romero et al. 2016)
  • Agro-technological practices (Garcia-Martinez et al. 2016; Rivero-Romero et al. 2016; Toledo and Barrera-Bassols 2017)
  • variations across ethnic groups (Perales, Benz, and Brush 2005; Brush and Perales 2007)

• La Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)
• 2000 indigenous population % by municipio (n = 2446 + 16)
• 68 indigenous macro-languages in Mexico (Instituto Nacional de Lenguas Indigenas (INALI))
  • In reality, many more distinct languages and dialects
  • 10-14% identify as indigenous, but only 6% speak an indigenous language
  • Stability is dubious
  • Loss of potentially irrecoverable deep climatic knowledge

Source: (Edmondson, 1971)

• Joseph Grinnell (Grinnell, 1917) - niches corresponded to species' environments
• Charles Elton (Elton, 1927) - associated species niches through biotic relationships
• George Evelyn Hutchinson - species role in environment (Hutchinson, 1957; Colwell and Rangel 2009).
  • The Hutchinsonian Niche - an “n-dimensional hypervolume” in ecological space in which a species can persist (Colwell and Rangel 2009; Holt 2009)
  • Fundmental Niche - A species' potential geographic distribution in true 4D space, constrained by its n-dimensional bioclimatic envelope
  • Realized Niche - fundamental niche constrained by 'bionomic' interactions; the niche that exists in reality and that does not overlap with other species niches (Griesemer 1992,Pulliam 2000)

Source: (Guisan et al., 2017) Credit: (Guisan et al., 2017,A. Townsend Peterson et al. 2011; Soberon & Peterson, 2005)

• G : Studied geographic area
  • A : Suitable abiotic environment (Grinellean niche)
  • B : Suitable biotic environment (Eltonian niche)
  • C : Colonizable range

1. Realize Niche, suitable to all three (Hutchinson's Niche)
2. Sutiable abiotic with unsuitable biotic conditions (e.g.: high competition)
3. Colonization outside of suitable environment (sink populations)
4. Sink in unsuitable biotic and abiotic environments (historical effects)

• Uses species presence and/or absence observation data and ecological data at those observation sites to estimate complex relationships of species niche (Elith et al. 2011).
• Project hyperdimensional ecological space onto 2- or 3D geographic space
• Numerous algorithms developed for and adapted to ENM
• Ensemble Ecological Niche Modelling weights individual models based on performance(Filho et. al, 2010, Araujo & New 2007)
• Reduce uncertainty across model type, model repetition, Pseudo-absence selection, etc.
• WA and Mean consensus methods provided significantly more robust predictions than single-models and the other consensus methods (Marimon et al., 2009)

• 22931 available through La Comision Nacional para el Conocimiento y Uso de la Biodiversidad (CONABIO)
• Cleaned records of erroneous records and those flagged 'Inconsistent'
• 18060 remaining records
• 64 unique maize races

• ANUSPLIN up to 60,000 weather stations (with >10 years data)
• largest available archive of downscaled climate data for ecology (Guisan et al., 2017)
• >2,700 citations for ecological studies including ENMs and SDMs (Booth et al., 2014)
• 30 arc-second resolution (~1 km²⁾
• Current (1970-2000) (v2.0) (Fick & Hijmans, 2017)
  • Maximum and Minimum Monthly Temperatures (°C * 10)
  • Monthly Precipitation (mm)
  • also includes solar radiation, windspeed, water vapor pressure
• Future RCP 8.5 (2041-2060, 2061-2080) (v1.4) (Hijmans et al., 2005)
  • dynamically-downscaled from 19 GCM used in CMIP5 (IPCC, 2013, Meehl et al., 2009)

• Derived from first ecological niche modelling alogorithm 'BIOCLIM' (Nix, 1986)
• More ecologically importance predictor variables
• Can reduce multicollineary, VIF; expands options
• Calculated from monthly Tmax, Tmin, Tmean, and Prec
• 19 Bioclimatic proxy variables from monthly means using 'dismo' (Hijmans et al., 2017)
• Potentially 16 'complementary' variables from 'envirem' package in R (Title & Bemmels, 2017)
  • Must use (WorldClim 2.0) current terrestial solar radiation for both current and future climatologies
  • Must average Tmin and Tmax to get Tmean (.99 R²⁾ (Title & Bemmels, 2017)

1970-2000 Bioclimatic Variables

• BIO1 = Annual Mean Temperature
• BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
• BIO3 = Isothermality (BIO2/BIO7) (* 100)
• BIO4 = Temperature Seasonality (standard deviation *100)
• BIO5 = Max Temperature of Warmest Month
• BIO6 = Min Temperature of Coldest Month
• BIO7 = Temperature Annual Range (BIO5-BIO6)
• BIO8 = Mean Temperature of Wettest Quarter
• BIO9 = Mean Temperature of Driest Quarter
• BIO10 = Mean Temperature of Warmest Quarter

• BIO11 = Mean Temperature of Coldest Quarter
• BIO12 = Annual Precipitation
• BIO13 = Precipitation of Wettest Month
• BIO14 = Precipitation of Driest Month
• BIO15 = Precipitation Seasonality (Coefficient of Variation)
• BIO16 = Precipitation of Wettest Quarter
• BIO17 = Precipitation of Driest Quarter
• BIO18 = Precipitation of Warmest Quarter
• BIO19 = Precipitation of Coldest Quarter

• Annual PET
• Thornthwaite Aridity Index
• Climatic Moisture Index
• Continentality
• EmbergerQ
• Growing Deg Days 0
• Growing Deg Days 5
• Growing Deg Days 10
• Max Temp Coldest
• Min Temp Warmest

• Month Count By Temp 10
• PET Coldest Quarter
• PET Driest Quarter
• PET seasonality
• PET Warmest Quarter
• PET Wettest Quarter
• Thermicity Index

• Terrain Indices from Elevation from WorldClim 1.4 via SRTM (Shuttle Radar Topographic Mission) with 'raster' package (Hijmans, 2017) (Wilson et al., 2007)
  • Aspect
  • Slope
  • Roughness (difference between the maximum and the minimum value of a cell and its 8 surrounding cells)

30 arc-second resolution (Fischer et al., 2008)

• Soil Quality Data (Factor, 'No or slight limitations' to 'Very severe limitations')
  1. Nutrient availability
  2. Nutrient retention capacity
  3. Rooting conditions
  4. Oxygen availability to roots
  5. Excess salts
  6. Toxicity
  7. Workability (constraining field management)

• Land cover (Continuous)
  1. rain-fed cultivated land
  2. irrigated cultivated land, according to GMIA 4.0
  3. total cultivated land
  4. forest land, calibrated to FRA2000 land statistics
  5. grass/scrub/woodland
  6. built-up land (residential and infrastructure)
  7. barren/very sparsely vegetated land
  8. Mapped Water Bodies

• Land Cover Classification
• Soil Quality 1-7
• 19 'dismo' bioclimatic variables
• 16 supplemental 'envirem' bioclimatic variables

Method

• 'usdm' R package for vif objects (Naimi et al., 2014)
• preferred over correlation thresholds due to 'hidden' correlation structures (Guisan et al., 2017)
• correlation structures may change over time
• select variables of ecological importance
• 'vifstep' with VIF threshold of 10 to get remaining variables (Guisan et al., 2017)

# using all data, no pre-selection
presvifstep@results

               Variables      VIF
1      BIO8MeanTWettestQ 3.169749
2  BIO13PrecWettestMonth 7.203552
3      BIO15PrecSeas.COV 5.426713
4      BIO18PrecWarmestQ 5.112587
5      BIO19PrecColdestQ 6.479971
6      EVMminTempWarmest 3.258270
7     EVMmonthCountByT10 2.206976
8         EVMPETColdestQ 5.539127
9          EVMPETDriestQ 2.507005
10        EVMPETWarmestQ 4.670706
11               IrrCult 1.169090
12          Rain.fedCult 1.599255
13        Grass.Woodland 2.314950
14                Barren 1.730909
15                 Urban 1.075483
16                 Water 1.107406
17                aspect 1.023113
18                 slope 1.446115